Classification of major species in the sericite-Artemisia desert grassland using hyperspectral images and spectral feature identification.

ABSTRACT:

Background

The species composition of and changes in grassland communities are important indices for inferring the number, quality and community succession of grasslands, and accurate monitoring is the foundation for evaluating, protecting, and utilizing grassland resources. Remote sensing technology provides a reliable and powerful approach for measuring regional terrain information, and the identification of grassland species by remote sensing will improve the quality and effectiveness of grassland monitoring.

Methods

Ground hyperspectral images of a sericite-Artemisia desert grassland in different seasons were obtained with a Soc710 VP imaging spectrometer. First-order differential processing was used to calculate the characteristic parameters. Analysis of v

SUBMITTER: Liu W 

PROVIDER: S-EPMC11260411 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature